Overview

Dataset statistics

Number of variables14
Number of observations178
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.6 KiB
Average record size in memory112.7 B

Variable types

Categorical1
Numeric13

Alerts

Alcohol is highly overall correlated with Class and 2 other fieldsHigh correlation
Class is highly overall correlated with Alcohol and 6 other fieldsHigh correlation
Color intensity is highly overall correlated with AlcoholHigh correlation
Flavanoids is highly overall correlated with Class and 5 other fieldsHigh correlation
Hue is highly overall correlated with Class and 2 other fieldsHigh correlation
Magnesium is highly overall correlated with ProlineHigh correlation
Malic acid is highly overall correlated with HueHigh correlation
Nonflavanoid phenols is highly overall correlated with FlavanoidsHigh correlation
OD280/OD315 of diluted wines is highly overall correlated with Class and 3 other fieldsHigh correlation
Proanthocyanins is highly overall correlated with Class and 3 other fieldsHigh correlation
Proline is highly overall correlated with Alcohol and 2 other fieldsHigh correlation
Total phenols is highly overall correlated with Class and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-04 03:30:28.776128
Analysis finished2023-12-04 03:30:39.877273
Duration11.1 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Class
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2
71 
1
59 
3
48 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters178
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Length

2023-12-03T21:30:39.934633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T21:30:39.999041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Most occurring characters

ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 178
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Most occurring scripts

ValueCountFrequency (%)
Common 178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.000618
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:40.107478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.6585
Q112.3625
median13.05
Q313.6775
95-th percentile14.2215
Maximum14.83
Range3.8
Interquartile range (IQR)1.315

Descriptive statistics

Standard deviation0.81182654
Coefficient of variation (CV)0.062445227
Kurtosis-0.85249957
Mean13.000618
Median Absolute Deviation (MAD)0.68
Skewness-0.051482331
Sum2314.11
Variance0.65906233
MonotonicityNot monotonic
2023-12-03T21:30:40.203208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.05 6
 
3.4%
12.37 6
 
3.4%
12.08 5
 
2.8%
12.29 4
 
2.2%
12.42 3
 
1.7%
12.25 3
 
1.7%
12 3
 
1.7%
12.33 2
 
1.1%
13.17 2
 
1.1%
13.73 2
 
1.1%
Other values (116) 142
79.8%
ValueCountFrequency (%)
11.03 1
0.6%
11.41 1
0.6%
11.45 1
0.6%
11.46 1
0.6%
11.56 1
0.6%
11.61 1
0.6%
11.62 1
0.6%
11.64 1
0.6%
11.65 1
0.6%
11.66 1
0.6%
ValueCountFrequency (%)
14.83 1
0.6%
14.75 1
0.6%
14.39 1
0.6%
14.38 2
1.1%
14.37 1
0.6%
14.34 1
0.6%
14.3 1
0.6%
14.23 1
0.6%
14.22 2
1.1%
14.21 1
0.6%

Malic acid
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3363483
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:40.293503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.061
Q11.6025
median1.865
Q33.0825
95-th percentile4.4555
Maximum5.8
Range5.06
Interquartile range (IQR)1.48

Descriptive statistics

Standard deviation1.1171461
Coefficient of variation (CV)0.47815905
Kurtosis0.29920668
Mean2.3363483
Median Absolute Deviation (MAD)0.52
Skewness1.0396512
Sum415.87
Variance1.2480154
MonotonicityNot monotonic
2023-12-03T21:30:40.368408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 7
 
3.9%
1.67 4
 
2.2%
1.81 4
 
2.2%
1.68 3
 
1.7%
1.61 3
 
1.7%
1.51 3
 
1.7%
1.9 3
 
1.7%
1.35 3
 
1.7%
1.53 3
 
1.7%
1.65 2
 
1.1%
Other values (123) 143
80.3%
ValueCountFrequency (%)
0.74 1
0.6%
0.89 1
0.6%
0.9 1
0.6%
0.92 1
0.6%
0.94 2
1.1%
0.98 1
0.6%
0.99 1
0.6%
1.01 1
0.6%
1.07 1
0.6%
1.09 1
0.6%
ValueCountFrequency (%)
5.8 1
0.6%
5.65 1
0.6%
5.51 1
0.6%
5.19 1
0.6%
5.04 1
0.6%
4.95 1
0.6%
4.72 1
0.6%
4.61 1
0.6%
4.6 1
0.6%
4.43 1
0.6%

Ash
Real number (ℝ)

Distinct79
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3665169
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:40.448819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.5575
95-th percentile2.7415
Maximum3.23
Range1.87
Interquartile range (IQR)0.3475

Descriptive statistics

Standard deviation0.27434401
Coefficient of variation (CV)0.11592734
Kurtosis1.1439782
Mean2.3665169
Median Absolute Deviation (MAD)0.16
Skewness-0.17669932
Sum421.24
Variance0.075264635
MonotonicityNot monotonic
2023-12-03T21:30:40.582509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3 7
 
3.9%
2.28 7
 
3.9%
2.7 6
 
3.4%
2.32 6
 
3.4%
2.36 6
 
3.4%
2.2 5
 
2.8%
2.38 5
 
2.8%
2.48 5
 
2.8%
2.1 4
 
2.2%
2.5 4
 
2.2%
Other values (69) 123
69.1%
ValueCountFrequency (%)
1.36 1
 
0.6%
1.7 2
1.1%
1.71 1
 
0.6%
1.75 1
 
0.6%
1.82 1
 
0.6%
1.88 1
 
0.6%
1.9 1
 
0.6%
1.92 3
1.7%
1.94 1
 
0.6%
1.95 1
 
0.6%
ValueCountFrequency (%)
3.23 1
0.6%
3.22 1
0.6%
2.92 1
0.6%
2.87 1
0.6%
2.86 1
0.6%
2.84 1
0.6%
2.8 1
0.6%
2.78 1
0.6%
2.75 1
0.6%
2.74 2
1.1%

Alcalinity of ash
Real number (ℝ)

Distinct63
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.494944
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:40.666305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.77
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.3395638
Coefficient of variation (CV)0.1713041
Kurtosis0.48794154
Mean19.494944
Median Absolute Deviation (MAD)2.05
Skewness0.21304689
Sum3470.1
Variance11.152686
MonotonicityNot monotonic
2023-12-03T21:30:40.780982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 15
 
8.4%
16 11
 
6.2%
21 11
 
6.2%
18 10
 
5.6%
19 9
 
5.1%
21.5 8
 
4.5%
18.5 7
 
3.9%
19.5 7
 
3.9%
22 7
 
3.9%
22.5 7
 
3.9%
Other values (53) 86
48.3%
ValueCountFrequency (%)
10.6 1
0.6%
11.2 1
0.6%
11.4 1
0.6%
12 1
0.6%
12.4 1
0.6%
13.2 1
0.6%
14 2
1.1%
14.6 1
0.6%
14.8 1
0.6%
15 2
1.1%
ValueCountFrequency (%)
30 1
 
0.6%
28.5 2
 
1.1%
27 1
 
0.6%
26.5 1
 
0.6%
26 1
 
0.6%
25.5 1
 
0.6%
25 5
2.8%
24.5 3
1.7%
24 5
2.8%
23.6 1
 
0.6%

Magnesium
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.741573
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:40.905093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.85
Q188
median98
Q3107
95-th percentile124.3
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.282484
Coefficient of variation (CV)0.14319489
Kurtosis2.1049913
Mean99.741573
Median Absolute Deviation (MAD)10
Skewness1.0981911
Sum17754
Variance203.98934
MonotonicityNot monotonic
2023-12-03T21:30:41.007730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 13
 
7.3%
86 11
 
6.2%
98 9
 
5.1%
101 9
 
5.1%
96 8
 
4.5%
102 7
 
3.9%
94 6
 
3.4%
85 6
 
3.4%
112 6
 
3.4%
97 5
 
2.8%
Other values (43) 98
55.1%
ValueCountFrequency (%)
70 1
 
0.6%
78 3
 
1.7%
80 5
 
2.8%
81 1
 
0.6%
82 1
 
0.6%
84 3
 
1.7%
85 6
3.4%
86 11
6.2%
87 3
 
1.7%
88 13
7.3%
ValueCountFrequency (%)
162 1
0.6%
151 1
0.6%
139 1
0.6%
136 1
0.6%
134 1
0.6%
132 1
0.6%
128 1
0.6%
127 1
0.6%
126 1
0.6%
124 1
0.6%

Total phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2951124
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:41.086241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.7425
median2.355
Q32.8
95-th percentile3.2745
Maximum3.88
Range2.9
Interquartile range (IQR)1.0575

Descriptive statistics

Standard deviation0.62585105
Coefficient of variation (CV)0.27268863
Kurtosis-0.83562652
Mean2.2951124
Median Absolute Deviation (MAD)0.505
Skewness0.086638586
Sum408.53
Variance0.39168954
MonotonicityNot monotonic
2023-12-03T21:30:41.187281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 8
 
4.5%
2.8 6
 
3.4%
3 6
 
3.4%
2.6 6
 
3.4%
2 5
 
2.8%
2.95 5
 
2.8%
1.65 4
 
2.2%
2.45 4
 
2.2%
2.85 4
 
2.2%
1.38 4
 
2.2%
Other values (87) 126
70.8%
ValueCountFrequency (%)
0.98 1
 
0.6%
1.1 1
 
0.6%
1.15 1
 
0.6%
1.25 1
 
0.6%
1.28 1
 
0.6%
1.3 1
 
0.6%
1.35 1
 
0.6%
1.38 4
2.2%
1.39 2
1.1%
1.4 2
1.1%
ValueCountFrequency (%)
3.88 1
 
0.6%
3.85 1
 
0.6%
3.52 1
 
0.6%
3.5 1
 
0.6%
3.4 1
 
0.6%
3.38 1
 
0.6%
3.3 3
1.7%
3.27 1
 
0.6%
3.25 2
1.1%
3.2 1
 
0.6%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0292697
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:41.281561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5455
Q11.205
median2.135
Q32.875
95-th percentile3.4975
Maximum5.08
Range4.74
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.99885869
Coefficient of variation (CV)0.4922257
Kurtosis-0.88038155
Mean2.0292697
Median Absolute Deviation (MAD)0.835
Skewness0.025343553
Sum361.21
Variance0.99771867
MonotonicityNot monotonic
2023-12-03T21:30:41.378256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 4
 
2.2%
2.03 3
 
1.7%
2.68 3
 
1.7%
0.6 3
 
1.7%
1.25 3
 
1.7%
0.58 3
 
1.7%
2.53 2
 
1.1%
0.47 2
 
1.1%
0.66 2
 
1.1%
2.92 2
 
1.1%
Other values (122) 151
84.8%
ValueCountFrequency (%)
0.34 1
0.6%
0.47 2
1.1%
0.48 1
0.6%
0.49 1
0.6%
0.5 2
1.1%
0.51 1
0.6%
0.52 1
0.6%
0.55 1
0.6%
0.56 1
0.6%
0.57 1
0.6%
ValueCountFrequency (%)
5.08 1
0.6%
3.93 1
0.6%
3.75 1
0.6%
3.74 1
0.6%
3.69 1
0.6%
3.67 1
0.6%
3.64 1
0.6%
3.56 1
0.6%
3.54 1
0.6%
3.49 1
0.6%

Nonflavanoid phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36185393
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:41.629404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.4375
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.12445334
Coefficient of variation (CV)0.34393253
Kurtosis-0.63719106
Mean0.36185393
Median Absolute Deviation (MAD)0.085
Skewness0.45015134
Sum64.41
Variance0.015488634
MonotonicityNot monotonic
2023-12-03T21:30:41.710712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.43 11
 
6.2%
0.26 11
 
6.2%
0.29 10
 
5.6%
0.32 9
 
5.1%
0.27 8
 
4.5%
0.3 8
 
4.5%
0.34 8
 
4.5%
0.4 8
 
4.5%
0.37 8
 
4.5%
0.24 7
 
3.9%
Other values (29) 90
50.6%
ValueCountFrequency (%)
0.13 1
 
0.6%
0.14 2
 
1.1%
0.17 5
2.8%
0.19 2
 
1.1%
0.2 2
 
1.1%
0.21 6
3.4%
0.22 6
3.4%
0.24 7
3.9%
0.25 2
 
1.1%
0.26 11
6.2%
ValueCountFrequency (%)
0.66 1
 
0.6%
0.63 4
2.2%
0.61 3
1.7%
0.6 3
1.7%
0.58 3
1.7%
0.56 1
 
0.6%
0.55 1
 
0.6%
0.53 7
3.9%
0.52 5
2.8%
0.5 5
2.8%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5908989
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:41.791509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.555
Q31.95
95-th percentile2.709
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.57235886
Coefficient of variation (CV)0.35977074
Kurtosis0.55464852
Mean1.5908989
Median Absolute Deviation (MAD)0.38
Skewness0.51713717
Sum283.18
Variance0.32759467
MonotonicityNot monotonic
2023-12-03T21:30:41.899110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 9
 
5.1%
1.46 7
 
3.9%
1.87 6
 
3.4%
1.25 5
 
2.8%
1.66 4
 
2.2%
2.08 4
 
2.2%
1.56 4
 
2.2%
1.98 4
 
2.2%
2.29 3
 
1.7%
1.14 3
 
1.7%
Other values (91) 129
72.5%
ValueCountFrequency (%)
0.41 1
0.6%
0.42 2
1.1%
0.55 1
0.6%
0.62 1
0.6%
0.64 2
1.1%
0.68 1
0.6%
0.73 2
1.1%
0.75 1
0.6%
0.8 2
1.1%
0.81 1
0.6%
ValueCountFrequency (%)
3.58 1
 
0.6%
3.28 1
 
0.6%
2.96 1
 
0.6%
2.91 2
1.1%
2.81 3
1.7%
2.76 1
 
0.6%
2.7 1
 
0.6%
2.5 1
 
0.6%
2.49 1
 
0.6%
2.45 1
 
0.6%

Color intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0580899
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:41.985408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.114
Q13.22
median4.69
Q36.2
95-th percentile9.598
Maximum13
Range11.72
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation2.3182859
Coefficient of variation (CV)0.45833228
Kurtosis0.38152227
Mean5.0580899
Median Absolute Deviation (MAD)1.51
Skewness0.86858479
Sum900.34
Variance5.3744494
MonotonicityNot monotonic
2023-12-03T21:30:42.071113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.6 4
 
2.2%
4.6 4
 
2.2%
3.8 4
 
2.2%
3.4 3
 
1.7%
5 3
 
1.7%
4.5 3
 
1.7%
5.4 3
 
1.7%
5.6 3
 
1.7%
3.05 3
 
1.7%
5.7 3
 
1.7%
Other values (122) 145
81.5%
ValueCountFrequency (%)
1.28 1
0.6%
1.74 1
0.6%
1.9 1
0.6%
1.95 2
1.1%
2 1
0.6%
2.06 2
1.1%
2.08 1
0.6%
2.12 1
0.6%
2.15 1
0.6%
2.2 1
0.6%
ValueCountFrequency (%)
13 1
0.6%
11.75 1
0.6%
10.8 1
0.6%
10.68 1
0.6%
10.52 1
0.6%
10.26 1
0.6%
10.2 1
0.6%
9.899999 1
0.6%
9.7 1
0.6%
9.58 1
0.6%

Hue
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95744944
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:42.169037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7825
median0.965
Q31.12
95-th percentile1.2845
Maximum1.71
Range1.23
Interquartile range (IQR)0.3375

Descriptive statistics

Standard deviation0.22857157
Coefficient of variation (CV)0.23872965
Kurtosis-0.34409574
Mean0.95744944
Median Absolute Deviation (MAD)0.165
Skewness0.021091272
Sum170.426
Variance0.052244961
MonotonicityNot monotonic
2023-12-03T21:30:42.274703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.04 8
 
4.5%
1.23 7
 
3.9%
1.12 6
 
3.4%
0.57 5
 
2.8%
0.89 5
 
2.8%
0.96 5
 
2.8%
1.25 5
 
2.8%
0.75 4
 
2.2%
1.05 4
 
2.2%
1.19 4
 
2.2%
Other values (68) 125
70.2%
ValueCountFrequency (%)
0.48 1
 
0.6%
0.54 1
 
0.6%
0.55 1
 
0.6%
0.56 2
 
1.1%
0.57 5
2.8%
0.58 2
 
1.1%
0.59 2
 
1.1%
0.6 3
1.7%
0.61 2
 
1.1%
0.62 1
 
0.6%
ValueCountFrequency (%)
1.71 1
 
0.6%
1.45 1
 
0.6%
1.42 1
 
0.6%
1.38 1
 
0.6%
1.36 2
 
1.1%
1.33 1
 
0.6%
1.31 2
 
1.1%
1.28 2
 
1.1%
1.27 1
 
0.6%
1.25 5
2.8%

OD280/OD315 of diluted wines
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6116854
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:42.344395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4625
Q11.9375
median2.78
Q33.17
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.2325

Descriptive statistics

Standard deviation0.70999043
Coefficient of variation (CV)0.27185144
Kurtosis-1.0864345
Mean2.6116854
Median Absolute Deviation (MAD)0.52
Skewness-0.3072855
Sum464.88
Variance0.50408641
MonotonicityNot monotonic
2023-12-03T21:30:42.417912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87 5
 
2.8%
1.82 4
 
2.2%
3 4
 
2.2%
2.78 4
 
2.2%
1.56 3
 
1.7%
1.75 3
 
1.7%
2.77 3
 
1.7%
2.31 3
 
1.7%
1.33 3
 
1.7%
3.33 3
 
1.7%
Other values (112) 143
80.3%
ValueCountFrequency (%)
1.27 1
 
0.6%
1.29 2
1.1%
1.3 1
 
0.6%
1.33 3
1.7%
1.36 1
 
0.6%
1.42 1
 
0.6%
1.47 1
 
0.6%
1.48 1
 
0.6%
1.51 2
1.1%
1.55 1
 
0.6%
ValueCountFrequency (%)
4 1
0.6%
3.92 1
0.6%
3.82 1
0.6%
3.71 1
0.6%
3.69 1
0.6%
3.64 1
0.6%
3.63 1
0.6%
3.59 1
0.6%
3.58 2
1.1%
3.57 1
0.6%

Proline
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.89326
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-03T21:30:42.503600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.55
Q1500.5
median673.5
Q3985
95-th percentile1297.25
Maximum1680
Range1402
Interquartile range (IQR)484.5

Descriptive statistics

Standard deviation314.90747
Coefficient of variation (CV)0.42162313
Kurtosis-0.24840311
Mean746.89326
Median Absolute Deviation (MAD)202.5
Skewness0.76782178
Sum132947
Variance99166.717
MonotonicityNot monotonic
2023-12-03T21:30:42.602783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680 5
 
2.8%
520 5
 
2.8%
625 4
 
2.2%
750 4
 
2.2%
630 4
 
2.2%
1035 3
 
1.7%
562 3
 
1.7%
495 3
 
1.7%
660 3
 
1.7%
510 3
 
1.7%
Other values (111) 141
79.2%
ValueCountFrequency (%)
278 1
0.6%
290 1
0.6%
312 1
0.6%
315 1
0.6%
325 1
0.6%
342 1
0.6%
345 2
1.1%
352 1
0.6%
355 1
0.6%
365 1
0.6%
ValueCountFrequency (%)
1680 1
0.6%
1547 1
0.6%
1515 1
0.6%
1510 1
0.6%
1480 1
0.6%
1450 1
0.6%
1375 1
0.6%
1320 1
0.6%
1310 1
0.6%
1295 1
0.6%

Interactions

2023-12-03T21:30:38.754302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.084564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.044333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.753491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.453318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.479349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.291623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.087400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.822511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.610430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.532949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.320150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.006621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.833223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.330796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.101150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.810688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.554720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.534809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.354088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.173955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.890161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.810612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.606558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.370918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.056345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.881656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.395472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.158696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.862346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.605035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.584283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.409580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.217736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.957528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.880122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.650675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.417433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.101561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.940145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.456023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.222893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.921477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.701535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.678152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.487072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.262671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.013469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.932470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.701377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.462670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.164073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:39.098141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.514920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.281906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.977212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.751662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.737600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.549939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.324364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.072364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.987892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.780249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.511853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.243254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:39.166173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.573067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.322627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.037551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.802279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.796029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.605323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.373563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.152911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.059634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.850418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.559691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.301262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:39.238013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.643533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.385122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.100619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.866943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.866936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.670952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.431693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.211428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.119448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.910111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.605493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.354357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:39.281312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.691592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.435462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.142780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.922776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.919802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.722707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.480573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.268459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.161024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.985615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.664241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.406441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:39.355595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.757006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.487526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.190594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.979879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.973608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.784072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.562767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.335430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.209086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.044227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.724429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.472116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:39.412735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.803965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.537068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.245835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.042992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.041836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.855494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.616507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.384352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.286562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.098124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.771740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.525092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:39.459832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.850360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.577507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.307064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.264820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.098254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.905837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.662042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.431028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.343661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.154967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.820384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.576999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:39.514364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.942737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.638479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.358080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.322644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.146873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.962131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.709262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.493628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.408600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.210033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.903698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.637625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:39.587976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:29.995475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:30.690470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:31.407100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:32.380302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:33.215376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.031497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:34.767882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:35.554702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:36.464974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.257884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:37.956743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-03T21:30:38.705212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-03T21:30:42.685921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Alcalinity of ashAlcoholAshClassColor intensityFlavanoidsHueMagnesiumMalic acidNonflavanoid phenolsOD280/OD315 of diluted winesProanthocyaninsProlineTotal phenols
Alcalinity of ash1.000-0.3070.3660.382-0.074-0.444-0.353-0.1700.3040.389-0.326-0.254-0.456-0.377
Alcohol-0.3071.0000.2440.5810.6350.295-0.0240.3660.140-0.1620.1030.1930.6340.311
Ash0.3660.2441.0000.2220.2830.079-0.0500.3610.2310.146-0.0070.0240.2530.132
Class0.3820.5810.2221.0000.131-0.855-0.617-0.2500.3470.474-0.744-0.571-0.576-0.727
Color intensity-0.0740.6350.2830.1311.000-0.043-0.4190.3570.2900.060-0.318-0.0310.4570.011
Flavanoids-0.4440.2950.079-0.855-0.0431.0000.5350.233-0.325-0.5440.7420.7300.4300.879
Hue-0.353-0.024-0.050-0.617-0.4190.5351.0000.036-0.560-0.2680.4850.3430.2080.439
Magnesium-0.1700.3660.361-0.2500.3570.2330.0361.0000.080-0.2370.0570.1740.5080.246
Malic acid0.3040.1400.2310.3470.290-0.325-0.5600.0801.0000.255-0.255-0.245-0.057-0.280
Nonflavanoid phenols0.389-0.1620.1460.4740.060-0.544-0.268-0.2370.2551.000-0.495-0.385-0.270-0.448
OD280/OD315 of diluted wines-0.3260.103-0.007-0.744-0.3180.7420.4850.057-0.255-0.4951.0000.5540.2530.687
Proanthocyanins-0.2540.1930.024-0.571-0.0310.7300.3430.174-0.245-0.3850.5541.0000.3080.667
Proline-0.4560.6340.253-0.5760.4570.4300.2080.508-0.057-0.2700.2530.3081.0000.419
Total phenols-0.3770.3110.132-0.7270.0110.8790.4390.246-0.280-0.4480.6870.6670.4191.000

Missing values

2023-12-03T21:30:39.671999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-03T21:30:39.801191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
0114.231.712.4315.61272.803.060.282.295.641.043.921065
1113.201.782.1411.21002.652.760.261.284.381.053.401050
2113.162.362.6718.61012.803.240.302.815.681.033.171185
3114.371.952.5016.81133.853.490.242.187.800.863.451480
4113.242.592.8721.01182.802.690.391.824.321.042.93735
5114.201.762.4515.21123.273.390.341.976.751.052.851450
6114.391.872.4514.6962.502.520.301.985.251.023.581290
7114.062.152.6117.61212.602.510.311.255.051.063.581295
8114.831.642.1714.0972.802.980.291.985.201.082.851045
9113.861.352.2716.0982.983.150.221.857.221.013.551045
ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
168313.582.582.6924.51051.550.840.391.548.6600000.741.80750
169313.404.602.8625.01121.980.960.271.118.5000000.671.92630
170312.203.032.3219.0961.250.490.400.735.5000000.661.83510
171312.772.392.2819.5861.390.510.480.649.8999990.571.63470
172314.162.512.4820.0911.680.700.441.249.7000000.621.71660
173313.715.652.4520.5951.680.610.521.067.7000000.641.74740
174313.403.912.4823.01021.800.750.431.417.3000000.701.56750
175313.274.282.2620.01201.590.690.431.3510.2000000.591.56835
176313.172.592.3720.01201.650.680.531.469.3000000.601.62840
177314.134.102.7424.5962.050.760.561.359.2000000.611.60560